We use a 2D convolutional neural network (CNN) for the architecture of the appearance model \(F\) . All the inputs to the network are converted to 2D feature maps according to a fixed UV mapping. Instead of the autoencoder structure of Codec Avatars [1]}, [2]}, we adopt the U-Net architecture [3]} in each of the three parts to learn an appearance function that focuses on local information around each input location in order to encourage generalization.
<FIGURE><FIGURE>